AutoLoop

What is AutoLoop?

AutoLoop is a Rust-native AIOS for governed agent execution.

It does not just call models and tools.
It turns ambiguous intent into a controlled runtime loop:

clarify → plan → gate → execute → verify → remember → replay → improve

That means:

• vague tasks become structured sessions
• all execution goes through policy and runtime guards
• results can be verified, audited, and replayed
• memory is not passive storage — it feeds future reasoning
• learning only upgrades when trust conditions are met

AutoLoop is for people who want more than "agent demos".
It is for building AI systems that can be governed.

Why AutoLoop exists

Most agent systems optimize for:

• more tools
• longer chains
• more autonomy
• prettier demos

AutoLoop optimizes for something else:

• controlled execution
• verifiable outcomes
• runtime governance
• learning with trust boundaries
• operator visibility and replay

In other words:

AutoLoop is not another free-form agent wrapper.
It is a governed execution runtime for AI systems.

5-Minute Demo

• Windows: demo/e2e-5min.ps1
• Linux: demo/e2e-5min.sh
• Demo recording checklist: demo/RECORDING_CHECKLIST.md

Quick Start

Prerequisites

• Rust toolchain
• Optional: SpacetimeDB CLI
• Optional: Docker / Docker Compose

Run

cargo run --manifest-path .\Cargo.toml -- --message "Build a swarm that uses graph memory and MCP execution" --swarm

Validate

cargo check --workspace --manifest-path .\Cargo.toml
cargo test --workspace --manifest-path .\Cargo.toml

Browser Research Runtime

Supported real research backends:

• browser_fetch: use a Browserless-style render endpoint
• playwright_cli: use local Node + Playwright for browser rendering
• firecrawl: use Firecrawl search/scrape APIs

Recommended health checks:

cargo run --manifest-path .\Cargo.toml -- system health
cargo run --manifest-path .\Cargo.toml -- crawl status --anchor-id cli:focus

Planning and Learning Governance Flow

┌──────────────────────────────────────────────┐
│              AutoLoop Core Loop              │
└──────────────────────────────────────────────┘

User Intent
    ↓
Understand
    ↓
Plan
    ↓
Execute (Guarded)
    ↓
Verify
    ↓
Learn
    ↓
Evolve
    ↓
Repeat ↺

Why AutoLoop (3 Core Differentiators)

1. Governed execution, not free-form agent calls: capabilities are cataloged, verified, and routed through runtime guardrails.
2. Memory that participates in decisions: GraphRAG + learning records feed routing, verifier, and capability evolution.
3. End-to-end operability: CLI runtime + SpacetimeDB persistence + dashboard + deployment templates in one repository.

What Is Implemented in v0.1.0-alpha

• Multi-turn requirement clarification with scope freeze signals
• CEO + planner/critic/judge orchestration artifacts
• Capability catalog and verifier-gated execution path
• GraphRAG snapshot and incremental merge pipeline
• Learning persistence for episodes, skills, causal edges, and witness logs
• Observability records and dashboard snapshot serving

Current Scope (Honest Boundaries)

• This is an engineering alpha, not a fully production-hardened autonomous platform.
• Real-world provider/tool integrations exist but still need broader compatibility and hardening.
• GraphRAG, verifier policy depth, and learning strategy are functional but still evolving.

Project Map

• Runtime source: src
• SpacetimeDB module: spacetimedb
• Adapter crate: autoloop-spacetimedb-adapter
• Dashboard UI: dashboard-ui
• Deployment assets: deploy
• Tests: tests

Docs

• Deep docs index: docs/README.md
• Process model (neutral naming): docs/PROCESS_MODEL.md
• P1-P13 unified protocol (AI output contract + layer flows): docs/P1_P13_UNIFIED_PROTOCOL.md
• Contracts RFC v1: docs/RFC_CONTRACTS_V1.md
• Gray rollout runbook: docs/ROLLOUT_RUNBOOK.md
• Architecture: ARCHITECTURE.md
• API summary: API.md
• Contributing: CONTRIBUTING.md
• Release notes: RELEASE_NOTES_v0.1.0-alpha.md
• Public issue backlog: docs/ISSUE_BACKLOG_v0.1.0-alpha.md

Notes

• Badges and links are already bound to rootkiller6788/AutoLoop.